HARSH LARIYA

DATA SCIENCE AND BUSINESS ANALYTICS INTERN @ THE SPARKS FOUNDATION

#GRIPJUNE21 #TSF #DATASCIENCE

EXPLORATORY DATA ANALYSIS - RETAIL

Problem statement:As a business manager, try to find out the weak areas where you can work to make more profit.

1.IMPORTING LIBRARIES

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

2.LOADING DATASET

df = pd.read_csv('C:\\Users\\91975\\Downloads\\SampleSuperstore.csv')

df.head()

	Ship Mode	Segment	Country	City	State	Postal Code	Region	Category	Sub-Category	Sales	Quantity	Discount	Profit
0	Second Class	Consumer	United States	Henderson	Kentucky	42420	South	Furniture	Bookcases	261.9600	2	0.00	41.9136
1	Second Class	Consumer	United States	Henderson	Kentucky	42420	South	Furniture	Chairs	731.9400	3	0.00	219.5820
2	Second Class	Corporate	United States	Los Angeles	California	90036	West	Office Supplies	Labels	14.6200	2	0.00	6.8714
3	Standard Class	Consumer	United States	Fort Lauderdale	Florida	33311	South	Furniture	Tables	957.5775	5	0.45	-383.0310
4	Standard Class	Consumer	United States	Fort Lauderdale	Florida	33311	South	Office Supplies	Storage	22.3680	2	0.20	2.5164

CHECKING NULL VALUES AND BASIC ANALYSIS OF DATASET

df.shape

(9994, 13)

df.isnull().sum()

Ship Mode       0
Segment         0
Country         0
City            0
State           0
Postal Code     0
Region          0
Category        0
Sub-Category    0
Sales           0
Quantity        0
Discount        0
Profit          0
dtype: int64

 df = df.drop_duplicates()

df.shape

(9977, 13)

df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 9977 entries, 0 to 9993
Data columns (total 13 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   Ship Mode     9977 non-null   object 
 1   Segment       9977 non-null   object 
 2   Country       9977 non-null   object 
 3   City          9977 non-null   object 
 4   State         9977 non-null   object 
 5   Postal Code   9977 non-null   int64  
 6   Region        9977 non-null   object 
 7   Category      9977 non-null   object 
 8   Sub-Category  9977 non-null   object 
 9   Sales         9977 non-null   float64
 10  Quantity      9977 non-null   int64  
 11  Discount      9977 non-null   float64
 12  Profit        9977 non-null   float64
dtypes: float64(3), int64(2), object(8)
memory usage: 1.1+ MB

df.columns

Index(['Ship Mode', 'Segment', 'Country', 'City', 'State', 'Postal Code',
       'Region', 'Category', 'Sub-Category', 'Sales', 'Quantity', 'Discount',
       'Profit'],
      dtype='object')

df.nunique()

Ship Mode          4
Segment            3
Country            1
City             531
State             49
Postal Code      631
Region             4
Category           3
Sub-Category      17
Sales           5825
Quantity          14
Discount          12
Profit          7287
dtype: int64

df.describe()

	Postal Code	Sales	Quantity	Discount	Profit
count	9977.000000	9977.000000	9977.000000	9977.000000	9977.00000
mean	55154.964117	230.148902	3.790719	0.156278	28.69013
std	32058.266816	623.721409	2.226657	0.206455	234.45784
min	1040.000000	0.444000	1.000000	0.000000	-6599.97800
25%	23223.000000	17.300000	2.000000	0.000000	1.72620
50%	55901.000000	54.816000	3.000000	0.200000	8.67100
75%	90008.000000	209.970000	5.000000	0.200000	29.37200
max	99301.000000	22638.480000	14.000000	0.800000	8399.97600

EXPLORATORY DATA ANAYSIS

ANALYSIS USING PAIRPLOTS

sns.pairplot(df)

<seaborn.axisgrid.PairGrid at 0x21190d1f160>

sns.pairplot(df,hue='Segment')

<seaborn.axisgrid.PairGrid at 0x21196dea6a0>

sns.pairplot(df,hue= 'Category')

<seaborn.axisgrid.PairGrid at 0x2119a82a580>

sns.pairplot(df,hue = 'Region')

<seaborn.axisgrid.PairGrid at 0x211a17997c0>

sns.pairplot(df,hue = 'Ship Mode')

<seaborn.axisgrid.PairGrid at 0x2119d7938b0>

sns.pairplot(df,hue = 'Sub-Category')

<seaborn.axisgrid.PairGrid at 0x2119f748bb0>

plt.figure(figsize=(8,5))
sns.kdeplot(x=df['Sales'],bw_method=25,color= 'Green')
sns.kdeplot(x=df['Profit'],bw_method=25)

<AxesSubplot:xlabel='Sales', ylabel='Density'>

Profit is more than that of sales and sales is widely spread

plt.scatter(df['Region'],df['Profit'])

<matplotlib.collections.PathCollection at 0x211a532a490>

plt.boxplot(df['Profit'])

{'whiskers': [<matplotlib.lines.Line2D at 0x211a5387040>,
  <matplotlib.lines.Line2D at 0x211a53873a0>],
 'caps': [<matplotlib.lines.Line2D at 0x211a5387700>,
  <matplotlib.lines.Line2D at 0x211a5387a60>],
 'boxes': [<matplotlib.lines.Line2D at 0x211a5379ca0>],
 'medians': [<matplotlib.lines.Line2D at 0x211a5387dc0>],
 'fliers': [<matplotlib.lines.Line2D at 0x211a5393160>],
 'means': []}

COUNT PLOTS

sns.countplot(x = 'Ship Mode',data=df)

<AxesSubplot:xlabel='Ship Mode', ylabel='count'>

Most used Ship Mode : Standard Class Least used Ship Mode: Same Day

sns.countplot(x='Region',data=df)

<AxesSubplot:xlabel='Region', ylabel='count'>

Region with most count : West Region with most count : South

sns.countplot(x= 'Segment',data = df)

<AxesSubplot:xlabel='Segment', ylabel='count'>

sns.countplot(x= 'Category',data = df)

<AxesSubplot:xlabel='Category', ylabel='count'>

sns.countplot(x= 'Sub-Category',data = df)
plt.xticks(rotation = 90)

(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16]),
 [Text(0, 0, 'Bookcases'),
  Text(1, 0, 'Chairs'),
  Text(2, 0, 'Labels'),
  Text(3, 0, 'Tables'),
  Text(4, 0, 'Storage'),
  Text(5, 0, 'Furnishings'),
  Text(6, 0, 'Art'),
  Text(7, 0, 'Phones'),
  Text(8, 0, 'Binders'),
  Text(9, 0, 'Appliances'),
  Text(10, 0, 'Paper'),
  Text(11, 0, 'Accessories'),
  Text(12, 0, 'Envelopes'),
  Text(13, 0, 'Fasteners'),
  Text(14, 0, 'Supplies'),
  Text(15, 0, 'Machines'),
  Text(16, 0, 'Copiers')])

Most bought sub category : Binders,Paper,Furnishings
Least bought sub category: Copiers,Machines,Supplies

plt.figure(figsize=(15,8))
sns.countplot(x='State',data=df)
plt.xticks(rotation= 90)

(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
        34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48]),
 [Text(0, 0, 'Kentucky'),
  Text(1, 0, 'California'),
  Text(2, 0, 'Florida'),
  Text(3, 0, 'North Carolina'),
  Text(4, 0, 'Washington'),
  Text(5, 0, 'Texas'),
  Text(6, 0, 'Wisconsin'),
  Text(7, 0, 'Utah'),
  Text(8, 0, 'Nebraska'),
  Text(9, 0, 'Pennsylvania'),
  Text(10, 0, 'Illinois'),
  Text(11, 0, 'Minnesota'),
  Text(12, 0, 'Michigan'),
  Text(13, 0, 'Delaware'),
  Text(14, 0, 'Indiana'),
  Text(15, 0, 'New York'),
  Text(16, 0, 'Arizona'),
  Text(17, 0, 'Virginia'),
  Text(18, 0, 'Tennessee'),
  Text(19, 0, 'Alabama'),
  Text(20, 0, 'South Carolina'),
  Text(21, 0, 'Oregon'),
  Text(22, 0, 'Colorado'),
  Text(23, 0, 'Iowa'),
  Text(24, 0, 'Ohio'),
  Text(25, 0, 'Missouri'),
  Text(26, 0, 'Oklahoma'),
  Text(27, 0, 'New Mexico'),
  Text(28, 0, 'Louisiana'),
  Text(29, 0, 'Connecticut'),
  Text(30, 0, 'New Jersey'),
  Text(31, 0, 'Massachusetts'),
  Text(32, 0, 'Georgia'),
  Text(33, 0, 'Nevada'),
  Text(34, 0, 'Rhode Island'),
  Text(35, 0, 'Mississippi'),
  Text(36, 0, 'Arkansas'),
  Text(37, 0, 'Montana'),
  Text(38, 0, 'New Hampshire'),
  Text(39, 0, 'Maryland'),
  Text(40, 0, 'District of Columbia'),
  Text(41, 0, 'Kansas'),
  Text(42, 0, 'Vermont'),
  Text(43, 0, 'Maine'),
  Text(44, 0, 'South Dakota'),
  Text(45, 0, 'Idaho'),
  Text(46, 0, 'North Dakota'),
  Text(47, 0, 'Wyoming'),
  Text(48, 0, 'West Virginia')])

sns.countplot(x='Quantity',data=df)
plt.xticks(rotation= 90)

(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13]),
 [Text(0, 0, '1'),
  Text(1, 0, '2'),
  Text(2, 0, '3'),
  Text(3, 0, '4'),
  Text(4, 0, '5'),
  Text(5, 0, '6'),
  Text(6, 0, '7'),
  Text(7, 0, '8'),
  Text(8, 0, '9'),
  Text(9, 0, '10'),
  Text(10, 0, '11'),
  Text(11, 0, '12'),
  Text(12, 0, '13'),
  Text(13, 0, '14')])

sns.countplot(x='Discount',data=df)
plt.xticks(rotation= 90)

(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11]),
 [Text(0, 0, '0.0'),
  Text(1, 0, '0.1'),
  Text(2, 0, '0.15'),
  Text(3, 0, '0.2'),
  Text(4, 0, '0.3'),
  Text(5, 0, '0.32'),
  Text(6, 0, '0.4'),
  Text(7, 0, '0.45'),
  Text(8, 0, '0.5'),
  Text(9, 0, '0.6'),
  Text(10, 0, '0.7'),
  Text(11, 0, '0.8')])

sns.countplot(x= 'Ship Mode',hue= 'Category',data = df)

<AxesSubplot:xlabel='Ship Mode', ylabel='count'>

plt.figure(figsize=(15,10))
sns.countplot(x= 'Category',hue ='Sub-Category',data = df )

<AxesSubplot:xlabel='Category', ylabel='count'>

plt.figure(figsize=(20,10))
sns.countplot(x= 'Sub-Category',hue= 'Region',data = df)

<AxesSubplot:xlabel='Sub-Category', ylabel='count'>

CORRELATION

corr = df.corr()

corr

	Postal Code	Sales	Quantity	Discount	Profit
Postal Code	1.000000	-0.023476	0.013110	0.059225	-0.029892
Sales	-0.023476	1.000000	0.200722	-0.028311	0.479067
Quantity	0.013110	0.200722	1.000000	0.008678	0.066211
Discount	0.059225	-0.028311	0.008678	1.000000	-0.219662
Profit	-0.029892	0.479067	0.066211	-0.219662	1.000000

sns.heatmap(corr, xticklabels=corr.columns, yticklabels=corr.columns, annot=True, cmap=sns.diverging_palette(220, 20, as_cmap=True))

<AxesSubplot:>

DISTRIBUTION

hist = df.hist(bins=10,figsize =(10,10))

boxplot = df.boxplot(grid=False, vert=False,fontsize=15)

sns.boxplot(y = 'Sales',data=df)

<AxesSubplot:ylabel='Sales'>

sns.lineplot(x='Discount',y='Profit',data = df,color='y',label ='Discount')
plt.legend()

<matplotlib.legend.Legend at 0x211c24d8f70>

sns.catplot(x='Region',y='Sales',data = df)

<seaborn.axisgrid.FacetGrid at 0x211a71c7a90>

plt.figure(figsize= (50,20))
sns.relplot(x=df['Ship Mode'],y= df['Sales'], hue= df['Category'])

<seaborn.axisgrid.FacetGrid at 0x211a728a700>

<Figure size 3600x1440 with 0 Axes>

sns.barplot(x=df['Region'],y=df['Sales'])

<AxesSubplot:xlabel='Region', ylabel='Sales'>

STATE WISE ANALYSIS

df['State'].value_counts()

California              1996
New York                1127
Texas                    983
Pennsylvania             586
Washington               502
Illinois                 491
Ohio                     468
Florida                  383
Michigan                 254
North Carolina           249
Arizona                  224
Virginia                 224
Georgia                  184
Tennessee                183
Colorado                 182
Indiana                  149
Kentucky                 139
Massachusetts            135
New Jersey               130
Oregon                   123
Wisconsin                110
Maryland                 105
Delaware                  96
Minnesota                 89
Connecticut               82
Missouri                  66
Oklahoma                  66
Alabama                   61
Arkansas                  60
Rhode Island              56
Mississippi               53
Utah                      53
South Carolina            42
Louisiana                 42
Nevada                    39
Nebraska                  38
New Mexico                37
Iowa                      30
New Hampshire             27
Kansas                    24
Idaho                     21
Montana                   15
South Dakota              12
Vermont                   11
District of Columbia      10
Maine                      8
North Dakota               7
West Virginia              4
Wyoming                    1
Name: State, dtype: int64

State_profit = df.groupby('State')['Profit'].sum()

State_profit

State
Alabama                  5786.8253
Arizona                 -3427.9246
Arkansas                 4008.6871
California              76330.7891
Colorado                -6527.8579
Connecticut              3511.4918
Delaware                 9977.3748
District of Columbia     1059.5893
Florida                 -3399.3017
Georgia                 16250.0433
Idaho                     826.7231
Illinois               -12601.6500
Indiana                 18382.9363
Iowa                     1183.8119
Kansas                    836.4435
Kentucky                11199.6966
Louisiana                2196.1023
Maine                     454.4862
Maryland                 7031.1788
Massachusetts            6785.5016
Michigan                24428.0903
Minnesota               10823.1874
Mississippi              3172.9762
Missouri                 6436.2105
Montana                  1833.3285
Nebraska                 2037.0942
Nevada                   3316.7659
New Hampshire            1706.5028
New Jersey               9772.9138
New Mexico               1157.1161
New York                74015.4622
North Carolina          -7490.9122
North Dakota              230.1497
Ohio                   -16959.3178
Oklahoma                 4853.9560
Oregon                  -1194.0993
Pennsylvania           -15565.4035
Rhode Island             7285.6293
South Carolina           1769.0566
South Dakota              394.8283
Tennessee               -5341.6936
Texas                  -25750.9833
Utah                     2546.5335
Vermont                  2244.9783
Virginia                18597.9504
Washington              33368.2375
West Virginia             185.9216
Wisconsin                8401.8004
Wyoming                   100.1960
Name: Profit, dtype: float64

plt.figure(figsize = (30,20))
plt.plot(State_profit)
plt.xticks(rotation =90,size=20)

([0,
  1,
  2,
  3,
  4,
  5,
  6,
  7,
  8,
  9,
  10,
  11,
  12,
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  14,
  15,
  16,
  17,
  18,
  19,
  20,
  21,
  22,
  23,
  24,
  25,
  26,
  27,
  28,
  29,
  30,
  31,
  32,
  33,
  34,
  35,
  36,
  37,
  38,
  39,
  40,
  41,
  42,
  43,
  44,
  45,
  46,
  47,
  48],
 [Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
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  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, '')])

plt.figure(figsize = (20,15))
sns.barplot(x= State_profit.index,y=Sales_profit.values)
plt.xticks(rotation = 90)

(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
        34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48]),
 [Text(0, 0, 'Alabama'),
  Text(1, 0, 'Arizona'),
  Text(2, 0, 'Arkansas'),
  Text(3, 0, 'California'),
  Text(4, 0, 'Colorado'),
  Text(5, 0, 'Connecticut'),
  Text(6, 0, 'Delaware'),
  Text(7, 0, 'District of Columbia'),
  Text(8, 0, 'Florida'),
  Text(9, 0, 'Georgia'),
  Text(10, 0, 'Idaho'),
  Text(11, 0, 'Illinois'),
  Text(12, 0, 'Indiana'),
  Text(13, 0, 'Iowa'),
  Text(14, 0, 'Kansas'),
  Text(15, 0, 'Kentucky'),
  Text(16, 0, 'Louisiana'),
  Text(17, 0, 'Maine'),
  Text(18, 0, 'Maryland'),
  Text(19, 0, 'Massachusetts'),
  Text(20, 0, 'Michigan'),
  Text(21, 0, 'Minnesota'),
  Text(22, 0, 'Mississippi'),
  Text(23, 0, 'Missouri'),
  Text(24, 0, 'Montana'),
  Text(25, 0, 'Nebraska'),
  Text(26, 0, 'Nevada'),
  Text(27, 0, 'New Hampshire'),
  Text(28, 0, 'New Jersey'),
  Text(29, 0, 'New Mexico'),
  Text(30, 0, 'New York'),
  Text(31, 0, 'North Carolina'),
  Text(32, 0, 'North Dakota'),
  Text(33, 0, 'Ohio'),
  Text(34, 0, 'Oklahoma'),
  Text(35, 0, 'Oregon'),
  Text(36, 0, 'Pennsylvania'),
  Text(37, 0, 'Rhode Island'),
  Text(38, 0, 'South Carolina'),
  Text(39, 0, 'South Dakota'),
  Text(40, 0, 'Tennessee'),
  Text(41, 0, 'Texas'),
  Text(42, 0, 'Utah'),
  Text(43, 0, 'Vermont'),
  Text(44, 0, 'Virginia'),
  Text(45, 0, 'Washington'),
  Text(46, 0, 'West Virginia'),
  Text(47, 0, 'Wisconsin'),
  Text(48, 0, 'Wyoming')])

Top 3 state where Profits are Highest: Califonia New York Washington

Top 3 state where Profits are Lowest: Texas Ohio Pennsylvania

df['State'].value_counts().mean()

203.9591836734694

#sales per state 
state_sales = df.groupby('State')['Sales'].sum().sort_values(ascending = False)

plt.figure(figsize = (20,10))
state_sales.plot.bar()

<AxesSubplot:xlabel='State'>

state_discount = df.groupby('State')['Discount'].sum().sort_values(ascending = False)

plt.figure(figsize = (20,10))
state_discount.plot.bar()

<AxesSubplot:xlabel='State'>

state_discount1 = df.groupby('State')['Discount'].mean().sort_values(ascending = False)

plt.figure(figsize = (20,10))
state_discount1.plot.bar()

<AxesSubplot:xlabel='State'>

Top 3 state where Sales are Highest. California New York Texas

Top 3 state where Sales are Lowest. North Dakota West Virginia Maine

df_state= df.groupby(['State'])[['Sales', 'Discount', 'Profit']].mean()
df_state.head(10)

	Sales	Discount	Profit
State
Alabama	319.846557	0.000000	94.865989
Arizona	157.508933	0.303571	-15.303235
Arkansas	194.635500	0.000000	66.811452
California	229.246629	0.072946	38.241878
Colorado	176.418231	0.316484	-35.867351
Connecticut	163.223866	0.007317	42.823071
Delaware	285.948635	0.006250	103.930988
District of Columbia	286.502000	0.000000	105.958930
Florida	233.612815	0.299347	-8.875461
Georgia	266.825217	0.000000	88.315453

df_state.tail(10)

	Sales	Discount	Profit
State
South Dakota	109.630000	0.000000	32.902358
Tennessee	167.551219	0.291257	-29.189583
Texas	173.066675	0.370539	-26.196321
Utah	211.699170	0.060377	48.047802
Vermont	811.760909	0.000000	204.088936
Virginia	315.342500	0.000000	83.026564
Washington	276.017550	0.064542	66.470593
West Virginia	302.456000	0.075000	46.480400
Wisconsin	291.951000	0.000000	76.380004
Wyoming	1603.136000	0.200000	100.196000

plt.plot(df_state.head(10))
plt.plot(df_state.tail(10))
plt.xticks(rotation= 90)

([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19],
 [Text(0, 0, ''),
  Text(0, 0, ''),
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  Text(0, 0, '')])

df_state.Sales.plot(kind='pie',figsize = (19,19),startangle=0,autopct='%1.1f%%',shadow=True)
plt.title('State wise analysis of Sale',fontsize=15)

Text(0.5, 1.0, 'State wise analysis of Sale')

States with highest amount of sales = Wyoming,Vermont,Nevada States with lowest amount of sales = South Dakota,Kansas,South Carolina

CITY wise

city_wise_top = df['City'].value_counts()
city_wise_top=city_wise_top.head(20)

city_wise_top.plot(kind='bar',figsize=(12,4))
plt.ylabel('Number of deals')
plt.xlabel('City')

plt.title('City Wise Dealings', fontsize = 15)
plt.show()

city_wise_bottom = df['City'].value_counts()
city_wise_bottom=city_wise_bottom.tail(20)

city_wise_bottom.plot(kind='bar',figsize=(10,5))
plt.ylabel('Number of deals')
plt.xlabel('City')

plt.title('City Wise Dealings', fontsize = 15)
plt.show()

df['City'].value_counts().mean()

18.789077212806028

city_profit_bottom= df.groupby('City')['Profit'].sum().sort_values().head(20)

plt.figure(figsize = (20,10))
city_profit_bottom.plot.bar()

<AxesSubplot:xlabel='City'>

city_profit_top= df.groupby('City')['Profit'].sum().sort_values(ascending=False).head(20)

plt.figure(figsize = (20,10))
city_profit_top.plot.bar()

<AxesSubplot:xlabel='City'>

city_sales_bottom = df.groupby('City')['Sales'].sum().sort_values().head(20)

plt.figure(figsize=(20,10))
city_sales_bottom.plot.bar()

<AxesSubplot:xlabel='City'>

city_sales_top = df.groupby('City')['Sales'].sum().sort_values(ascending=False).head(20)

plt.figure(figsize=(20,10))
city_sales_top.plot.bar()

<AxesSubplot:xlabel='City'>

city_wise= df.groupby(['City'])[['Sales', 'Discount', 'Profit']].mean()
city_wise= city_wise.sort_values('Profit')
city_wise.head()

	Sales	Discount	Profit
City
Bethlehem	337.926800	0.380000	-200.619160
Champaign	151.960000	0.600000	-182.352000
Oswego	107.326000	0.600000	-178.709200
Round Rock	693.436114	0.274286	-169.061614
Lancaster	215.031826	0.315217	-157.371052

city_wise.tail()

	Sales	Discount	Profit
City
Appleton	835.655000	0.0	277.383150
Minneapolis	733.501739	0.0	296.721070
Lafayette	807.619355	0.0	323.173794
Independence	1208.685000	0.0	487.831500
Jamestown	2354.395000	0.0	642.885750

Segment wise analysis of Profit, Discount and sell

df.Segment.value_counts()

Consumer       5191
Corporate      3020
Home Office    1783
Name: Segment, dtype: int64

segment = df.groupby('Segment')[['Profit','Sales','Discount']].sum()

segment1=df.groupby('Segment')[['Profit','Sales','Discount']].mean()

segment.plot.pie(subplots=True, 
                    autopct='%1.1f%%',
                    figsize=(18, 20),
                    startangle=90,     # start angle 90° (Africa)
                    shadow=True,
                    labels = segment.index)
plt.title('Segment wise analysis of Sale, Discount, profit')

Text(0.5, 1.0, 'Segment wise analysis of Sale, Discount, profit')

segment1.plot.pie(subplots=True, 
                    autopct='%1.1f%%',
                    figsize=(18, 20),
                    startangle=90,     # start angle 90° (Africa)
                    shadow=True,
                    normalize=True,
                    labels = segment1.index)
plt.title('Segment wise analysis of Sale, Discount, profit')

Text(0.5, 1.0, 'Segment wise analysis of Sale, Discount, profit')

QUANTITY WISE SALES, PROFIT AND DISCOUNT ANALYSIS

quantity_total =df.groupby(['Quantity'])[['Sales', 'Discount', 'Profit']].sum()

quantity_wise = df.groupby(['Quantity'])[['Sales', 'Discount', 'Profit']].mean()

quantity_wise

	Sales	Discount	Profit
Quantity
1	59.234632	0.152959	8.276396
2	120.377982	0.154844	16.027769
3	175.630896	0.153321	23.720306
4	272.779827	0.158373	37.227971
5	337.936339	0.157146	40.257394
6	362.101960	0.166556	18.051517
7	395.888393	0.161980	56.579163
8	458.210802	0.171595	42.244342
9	498.083683	0.147946	68.557716
10	422.046737	0.190702	35.862404
11	463.896529	0.097059	126.275503
12	646.452522	0.113043	51.524609
13	1197.710704	0.157407	235.144481
14	452.826138	0.151724	93.931166

quantity_wise.plot.pie( subplots=True,figsize=(21,21),autopct='%1.1f%%', pctdistance=0.81,startangle=90,shadow=True,labels = quantity_wise.index)
plt.title('Quantity wise analysis of Sale, Discount, profit')

Text(0.5, 1.0, 'Quantity wise analysis of Sale, Discount, profit')

quantity_total['Profit'].plot.pie(figsize=(10,10),autopct='%1.1f%%',pctdistance=0.81,startangle=0,shadow=True,labels=quantity_total.index)
plt.legend()
plt.title('Overall profit quantity wise')

Text(0.5, 1.0, 'Overall profit quantity wise')

SUB CATEGORY WISE

sub_category_wise = df.groupby('Sub-Category')[['Profit','Sales','Discount']].sum()

sub_category_wise.plot.bar(figsize=(20,10))

<AxesSubplot:xlabel='Sub-Category'>

pd.crosstab(df['Region'],df['Sub-Category']).plot(kind = 'bar',figsize=(15,10))

<AxesSubplot:xlabel='Region'>

profits = df.groupby('Sub-Category')[['Profit','Sales','Discount']].sum()

plt.figure(figsize = (14,16))
plt.pie(sub_category_wise['Sales'], labels = sub_category_wise.index, autopct = '%1.1f%%')
plt.title('Sub-Category Wise Sales Analysis', fontsize = 18)
plt.legend()
plt.xticks(rotation = 90)
plt.show()

plt.figure(figsize = (15,15))
plt.pie(sub_category_wise['Discount'], labels = sub_category_wise.index, autopct = '%1.1f%%')
plt.title('Sub-Category Wise Discount Analysis', fontsize = 20)
plt.legend()
plt.xticks(rotation = 90)
plt.show()

CATEGORY WISE

category_wise = df.groupby('Category')[['Profit','Sales','Discount']].sum()

category_wise.plot.bar(figsize=(10,10))

<AxesSubplot:xlabel='Category'>

category_wise_mean= df.groupby('Category')[['Profit','Sales','Discount']].mean()

category_wise_mean.plot.bar(figsize=(10,10))

<AxesSubplot:xlabel='Category'>

pd.crosstab(df['Region'],df['Category']).plot(kind = 'bar',figsize=(8,6))

<AxesSubplot:xlabel='Region'>

plt.figure(figsize = (10,10))
plt.pie(category_wise['Sales'], labels = category_wise.index, autopct = '%1.1f%%')
plt.title('Category Wise Sales Analysis', fontsize = 18)
plt.legend()
plt.xticks(rotation = 90)
plt.show()

REGION WISE

region_wise = df.groupby('Region')[['Profit','Sales','Discount']].sum()

region_wise

	Profit	Sales	Discount
Region
Central	39655.8752	500782.8528	557.14
East	91506.3092	678435.1960	413.50
South	46749.4303	391721.9050	238.55
West	108329.8079	725255.6365	350.00

region_wise.plot.bar()

<AxesSubplot:xlabel='Region'>

region_wise_mean = df.groupby('Region')[['Profit','Sales','Discount']].mean()

region_wise_mean.plot.bar()

<AxesSubplot:xlabel='Region'>

region_wise.plot.pie(subplots=True, 
                   figsize=(18, 20), 
                   autopct='%1.1f%%',
                   labels = region_wise.index)

array([<AxesSubplot:ylabel='Profit'>, <AxesSubplot:ylabel='Sales'>,
       <AxesSubplot:ylabel='Discount'>], dtype=object)

region_wise_mean.plot.pie(subplots=True, 
                   figsize=(18, 20), 
                   autopct='%1.1f%%',
                   labels = region_wise_mean.index)

C:\Users\91975\anaconda3\lib\site-packages\pandas\plotting\_matplotlib\core.py:1547: MatplotlibDeprecationWarning: normalize=None does not normalize if the sum is less than 1 but this behavior is deprecated since 3.3 until two minor releases later. After the deprecation period the default value will be normalize=True. To prevent normalization pass normalize=False 
  results = ax.pie(y, labels=blabels, **kwds)

array([<AxesSubplot:ylabel='Profit'>, <AxesSubplot:ylabel='Sales'>,
       <AxesSubplot:ylabel='Discount'>], dtype=object)

ship mode wise

shipmode_wise = df.groupby(['Ship Mode'])[['Sales', 'Discount', 'Profit']].sum()

shipmode_wise.plot.bar()

<AxesSubplot:xlabel='Ship Mode'>

shipmode_wise.plot.pie(subplots=True,
                     figsize=(18, 20), 
                     autopct='%1.1f%%', 
                     labels = shipmode_wise.index)

array([<AxesSubplot:ylabel='Sales'>, <AxesSubplot:ylabel='Discount'>,
       <AxesSubplot:ylabel='Profit'>], dtype=object)

RESULT AND CONCLUSION

Profit is more than that of sales and sales is widely spread.

Most widely used Ship Mode is Standard Class and the least is Same Day.

Most orders are from West region and the least is from South Region.

Consumer segment bought most products while Home offices bought the least.

Most sold Category is office supplies and the least is Technology.

Most sold Sub Categories : Binders, Paper, Furnishings.

Least sold Sub Categories: Copiers, Machines, Supplies.

Maximum people did not get discount while the minimum people got discount of 0.45.

Most ordered category is Office Supplies and the least ordered is Technology.

Most sold item in each category :

Furniture - furnishings.

Office supplies - Binders.

Technology - Phones.

Least sold item in each category :

Furniture - Bookcases.

Office supplies - Supplies.

Technology - Machines.

Most sold item is Binders in West and the least is Copiers in South.

Correlations:

Sales and Profit are Moderately Correlated.

Quantity and Profit are less Moderately Correlated.

Discount and Profit are Negatively Correlated

States wise Analysis :

States with most Profit : California, New York, Washington.

States with least Profit: Texas, Ohio, Pennsylvania.

States with most sales : California, New York, Texas.

States with least sales: Wyoming, West Virginia, North Dakota.

States with most Discount : Texas, Pennsylvania, Illinois.

States with least Discount: Alabama, Missouri, Maine and 22 other.

Mean sales is maximum for Wyoming and least for South Dakota.

City Wise Analysis:

Cities with maximum number of counts: New York City, Los Angeles, Philadelphia.

Cities with minimum number of counts: Commerce City, Aberdeen, Orland Park.

Cities with maximum Profit: New York City, Los Angeles, Seattle.

Cities with minimum Profit: Philadelphia, Houston, San-Antonio.

Cities with maximum Sales: New York City, Los Angeles, Seattle.

Cities with minimum Sales: Abilene, Elyria, Jupiter.

Segment wise Analysis: Maximum sales and profit are from Home Offices while the least is Consumer segment.

Quantity wise Analysis: Most people bought items in a triplet (20%) and least people bought 12(0.4%) and more.

Sub Category wise Analysis :

Most sold Category : Phones(14.4%), Chairs(14.3%), Storage(9.7%). Least sold Category : Fasteners, Labels, Envelopes.

Most Profitable Category : Copies, Phones, Accessories.

Least Profitable Category: Tables, Bookcases, Supplies.

Tables and bookcases are most sold but with the least profit.

Binders are the most sold item in every region.

Most discounts are on binders and the least is on copier.

Technology has most profit and sales.

Region Wise Analysis : West region has highest number of sales and high Profit . Central Region has lowest Profit. South Region Has the lowest Sales.